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63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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import torch.multiprocessing as mp
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from llamafactory.v1.accelerator.interface import DistributedInterface
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from llamafactory.v1.config.model_args import ModelArguments
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from llamafactory.v1.core.model_engine import ModelEngine
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from llamafactory.v1.plugins.model_plugins.parallelization.sequence_parallel import (
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SequenceParallelModelPlugin,
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sequence_parallel_loss,
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)
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from llamafactory.v1.utils.env import find_available_port
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from llamafactory.v1.utils.pytest import dist_env
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def _test_sequence_parallel_loss(local_rank: int, world_size: int, master_port: int, cp_size: int, dp_size: int):
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with dist_env(local_rank, world_size, master_port):
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model_args = ModelArguments(model="llamafactory/tiny-random-qwen3")
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# Initialize distributed interface with config
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dist_config = {"cp_mode": "ulysses", "cp_size": cp_size, "dp_size": dp_size}
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DistributedInterface(dist_config)
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# Now create model engine
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model_engine = ModelEngine(model_args=model_args)
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# Apply sequence parallel plugin
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SequenceParallelModelPlugin(dist_config.get("cp_mode", "ulysses"))(model_engine.model, dist_config)
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model_inputs = {
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"input_ids": torch.tensor([[1, 2, 3, 4, 5]]),
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"labels": torch.tensor([[1, 2, 3, 4, 5]]),
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"attention_mask": torch.tensor([[1, 1, 1, 1, 1]]),
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"position_ids": torch.tensor([[1, 2, 3, 4, 5]]),
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"loss_weights": torch.tensor([[1.0, 1.0, 1.0, 1.0, 1.0]]),
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}
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loss = sequence_parallel_loss(model_engine.model, model_inputs)
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assert loss is not None
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@pytest.mark.runs_on(["cuda", "npu"])
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@pytest.mark.require_distributed(2)
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@pytest.mark.parametrize("cp_size, dp_size", [(2, 1)])
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def test_sequence_parallel_loss(cp_size, dp_size):
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master_port = find_available_port()
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world_size = cp_size * dp_size
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mp.spawn(_test_sequence_parallel_loss, args=(world_size, master_port, cp_size, dp_size), nprocs=world_size)
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